Cannabis and Schizophrenia: Is there a link?
Bibliographic record
Abstract
Schizophrenia is serious mental disorder that affects an estimated 1 in 100 Canadians and their families. Typically, those suffering from schizophrenia start to present a wide variety of symptoms, including delusions, hallucinations, disturbances of thought, and social withdrawal, between the ages of 15 to 25. The role of cannabis as a causal risk factor for the development of schizophrenia remains largely debatable. In the last decade, countless studies have been performed to determine whether cannabis use in adolescence is associated with the development of schizophrenia in adulthood. The objective is to determine whether there is an association between cannabis use in adolescence and later development of schizophrenia by reviewing several pieces of epidemiological literature. By conducting a literature review, we were able to identify a number of epidemiological studies that assessed the relationship between cannabis use and the development of schizophrenia. Through the use of keywords, such as “schizophrenia”, “adulthood”, “adolescence”, and “cannabis”, we were able to obtain and analyze 12 studies published in scholarly peer-reviewed journals located on PubMed, Medline, and The University of Ottawa Library. In our review, we included only the most recent studies conducted after 2009 and included studies only written in English. 12 studies were analyzed to provide evidence that there is an association between the use of cannabis in adolescence and development of schizophrenia in those that are vulnerable to its effects. Individuals were more likely to develop schizophrenia if they smoked at an earlier age (<18 years), had a genetic predisposition, and/or had high cumulative exposure. The current literature indicates that cannabis is not a necessary nor sufficient cause of schizophrenia but evidence demonstrates that a history of cannabis use in adolescence can increase the risk of developing schizophrenia in vulnerable individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".